System and method for real-time impact assessment of social media posts with generative artificial inteligence

Abstract

A computerized-method for dynamically prioritizing social-media interactions in real-time, based on social-media posts in feeds, in a contact-center. The computerized-method includes: (i) monitoring by processors the social-media posts in the feeds of one or more social-media platforms which are integrated to the contact center. The social-media posts have been published during a preconfigured period, for each social-media post of a customer in each feed in the feeds: a. calculating a quality-score by operating an Artificial intelligence (AI) driven Content Quality Analysis (ACQA) module; and b. calculating a social-impact score based on the calculated quality score and one or more parameters; (ii) automatically prioritizing the social-media posts based on the calculated social-impact score to yield a priorities queue of social-media interactions. Each social-media post represents a social-media interaction, and (iii) automatically routing social-media interactions by a routine-engine to an available agent based on the yielded priorities queue of social-media interactions.

What is claimed: 
     
         1 . A computerized-method for dynamically prioritizing social-media interactions in real-time, based on social-media posts in feeds, in a contact center, said computerized-method comprising:
 (i) monitoring by one or more processors the social-media posts in the feeds of one or more social-media platforms which are integrated to the contact center,
 wherein said social-media posts have been published during a preconfigured period, for each social-media post of a customer in each feed in the feeds: 
 a. calculating a quality-score by operating an Artificial intelligence (AI) driven Content Quality Analysis (ACQA) module; and 
 b. calculating a social-impact score based on the calculated quality score and one or more parameters; 
   (ii) automatically prioritizing the social-media posts based on the calculated social-impact score to yield a priorities queue of social-media interactions,
 wherein each social-media post represents a social-media interaction, and 
   (iii) automatically routing social-media interactions by a routine-engine to an available agent based on the yielded priorities queue of social-media interactions.   
     
     
         2 . The computerized-method of  claim 1 , wherein said ACQA module comprising:
 (i) preprocessing text and media elements in the social-media post;   (ii) analyzing the preprocessed text and the preprocessed media elements to yield one or more factors of quality;   (iii) constructing a prompt for Large Language Model (LLM),   wherein the prompt includes the text of the post and instructions to assess the one or more factors of quality;   (iv) sending the constructed prompt to be executed via an Application Programming Interface (API) platform of the LLM and receiving a response;   (v) parsing the response to extract one or more quality-scores,   wherein the response is a string of text that includes a quality-score for each quality factor in the one or more factors of quality; and   (vi) calculating a total-content quality score, by summing the one or more quality-scores based on each quality-score preconfigured weight.   
     
     
         3 . The computerized-method of  claim 2 , wherein said one or more factors of quality includes at least one of: (i) relevance; (ii) accuracy; (iii) clarity; (iv) sentiment; and (v) total-quality. 
     
     
         4 . The computerized-method of  claim 1 , wherein said one or more parameters comprising at least one of:
 (i) user reach;   (ii) engagement metrics;   (iii) social-media post relevance;   (iv) social-media post accuracy;   (v) social-media post clarity;   (vi) response time sensitivity;   (vii) customer emotion intensity;   (viii) contextual keywords;   (ix) customer sentiment;   (x) customer loyalty; and   (xi) customer feedback.   
     
     
         5 . The computerized-method of  claim 4 , wherein said customer loyalty parameter of the customer is retrieved by the computerized-method further comprising: operating a social-media-feeds computation module, said social-media-feeds computation module comprising:
 retrieving the customer loyalty parameter of the customer, from a customers-database, and wherein the computerized-method further comprising:   a. constructing a social-impact-prompt LLM, that includes the text of the social-media post and   b. instructions to assess at least one parameter of: (i) user reach; (ii) engagement metrics; (iii) social-media post relevance; (iv) social-media post accuracy; (v) social-media post clarity; (vi) response time sensitivity; (vii) customer emotion intensity; (viii) contextual keywords; (ix) customer sentiment; and (x) customer feedback; and b. sending the constructed social-impact-prompt to be executed via an API platform of the LLM and receiving a response that includes a score for each parameter.   
     
     
         6 . The computerized-method of  claim 1 , wherein said processing of the text includes at least one of: (i) tokenizing; (ii) lowercasing; (iii) removing punctuation and stop-word; (iv) text-feature extraction,
 wherein said text-feature extraction includes at least one of: (i) word frequency; (ii) Term Frequency-Inverse Document Frequency (TF-IDF); (iii) word embeddings; and (iv) contextual embeddings.   
     
     
         7 . The computerized-method of  claim 2 , wherein said processing media elements includes at least one of: (i) resizing; (ii) normalization; and (iii) visual-feature extraction, wherein said visual-feature extraction is operated by at least one of: (i) Convolutional Neural Networks (CNNs); and (ii) pretrained models. 
     
     
         8 . The computerized-method of  claim 2 , wherein said analyzing of the preprocessed text is performed by applying at least one of: (i) Natural Language Processing (NLP); (ii) sentiment analysis; and (iii) readability analysis, and
 wherein said analyzing of the preprocessed media elements is operated by visual content analysis, said visual content analysis includes at least one technique of: (i) object detections; (ii) image classification; and (iii) content moderation.   
     
     
         9 . The computerized-method of  claim 1 , wherein said computerized-method is further comprising normalizing the calculated total-content quality score to a standardized scale. 
     
     
         10 . The computerized-method of  claim 2 , wherein said LLM is continuously trained using labeled data updates to adapt to evolving content types and quality standards over time. 
     
     
         11 . The computerized-method of  claim 1 , wherein said computerized-method is further comprising forwarding the calculated social-impact score of each social-media post and the related social-media post to a recommendation engine, said recommendation engine comprising: sending the calculated social-impact score of each social-media post and the related social-media post to at least one of: (i) knowledgebase; (ii) agent-dashboard; (iii) reporting module; and (iv) supervisor dashboard. 
     
     
         12 . The computerized-method of  claim 5 , where said calculated social-impact score is according to formula I:
   social-impact score=Σ(user reach* W 1)+(engagement metrics* W 2)+(social media interaction relevance* W 3)+(social media post accuracy* W 4)+(social media post clarity* W 5)+(response time sensitivity* W 6)+(customer emotion intensity* W 7)+(contextual keywords* W 8)+(customer sentiment* W 9)+(customer loyalty* W 10)+(customer feedback* W 11),  (I)
   whereby:   the user reach is a parameter that measures an influence of the customer,   the engagement metrics is a parameter that assesses level of engagement generated by the social-media post,   the social-media post relevance is a parameter that evaluates relevance of the social-media post to objectives of the contact center,   the social-media post accuracy is a parameter that refers to correctness of information presented in the social-media post,   the social-media post clarity is a parameter that assesses readability and comprehensibility of language used in the social-media post,   the time sensitivity is a parameter that indicates urgency of the content of the social-media post,   the customer emotion intensity is a parameter that evaluates strength of emotions expressed in the social-media post,   the contextual keywords is a parameter that provides an analysis of presence of keywords relevant to domain of the contact center,   the customer sentiment is a parameter that assesses overall sentiment of the social-media post,   the customer loyalty is a parameter that measures loyalty of the customer,   the customer feedback is a parameter that captures response of other customers to the social-media post, and   W1-W11 are preconfigured weights assigned to the parameters.   
     
     
         13 . A computerized-system for dynamically prioritizing social-media interactions in real-time, based on social-media posts in feeds, in a contact center, said computerized-system comprising:
 one or more processors, said one or more processors are configured to:   (i) monitor by one or more processors the social-media posts in the feeds of one or more social-media platforms which are integrated to the contact center,
 wherein said social-media posts have been published during a preconfigured period; 
   (ii) for each social-media post of a customer in each feed in the feeds:
 a. calculate a quality-score by operating an Artificial intelligence (AI) driven Content Quality Analysis (ACQA) module; and 
 b. calculate a social-impact score based on the calculated quality score and one or more parameters; 
   (iii) automatically prioritize the social-media posts based on the calculated social-impact score to yield a priorities queue of social-media interactions,
 wherein each social-media post represents a social-media interaction, and 
   (iv) automatically route social-media interactions by a routine-engine to an available agent based on the yielded priorities queue of social-media interactions.